Wednesday morning we had a very interesting invited session on industry dynamics in which, among others, one of my papers was presented. In this paper we develop a dynamic oligopoly model of the US penicillin industry after WWII. Dynamic models in the style of Ericson and Pakes (1995) are very powerful but have the drawback that they blow up very easily and become intractable. In a Markov-perfect equilibrium, even if only the immediate past matters, you have to compute transition probabilities for every firm and every possible firm state. In our model, for example, with only three states (a firm can either be inactive, active as an inefficient incumbent, or active as an efficient incumbent) this means (without invoking further assumptions) you have to compute a matrix with entries.

Thus, in these types of models it’s all about the possibility to reduce the state space. Ifrach and Gabriel now propose a very interesting approach in which you don’t follow the actions of all firms in a market but only of a few dominant firms. You pool the remaining firms together in a fringe of which only keep track of an aggregate moment. Think of, for example, the number of firms in the fringe or the aggregate production capacity. This makes life much easier.

It’s not trivial to incorporate this idea into the Markov-perfect equilibrium framework. A substantial part of the paper deals with these technicalities. What’s interesting, however, is that the transition from being a fringe firm to becoming dominant and vice versa is endogenous in the model. Another argument in favor of the approach is a behavioral one: in a large industry, maybe firms behave exactly like the model predicts, in the sense that they only keep track of their most important rivals and only have a vague idea about the rest. My time in the strategy department of BASF makes me buy this argument. Competitive intelligence is an important business of strategic units. But you don’t investigate every small player in very detail. At least not when you’re a dominant big player yourself.

Fabian Waldinger was featured on this blog before and I enjoy his work very much. This paper was presented in a session on economic history and innovation. And boy was the room packed. Might be because Petra Moser was the session chair, I don’t know. The quality of the papers certainly justified the number of listeners.

We know that science is very dependent on knowledge spillovers and the free flow of ideas between individuals. Science is also very international. The Annual Congress of the European Economic Association is a perfect example for that. During WWI, however, Europe was suddenly divided in two large blocks. Following the nationalist sentiment of the time, scientists committed themselves to boycott publications from the other camp as well as not to reveal own findings to the other side. International journal subscriptions were cancelled, no joint conferences were organized and research collaborations were abandoned.

The paper’s data show that this boycott was (in a negative sense) very effective. In the Central camp, before the war a paper coming from your own camp was 4 times more likely to be cited than a paper from the other side. This is not surprising since you’re more likely to cite ideas to which you’re immediately exposed to. During the war and the years after, this citation premium for own-side papers increases by 100%, making it 8 times more likely to be cited.

The authors show that citation to papers before 1914 remain at the same level. This suggests that the boycott not simply induced a reluctance to cite papers that were read nevertheless, but the flow of ideas was effectively shut down. After the boycott, the accumulated flow of ideas partly reached the other side with a delay. But citations of papers from the immediate war-time never caught up completely. The authors suggest that this sudden stop of knowledge transfer affected scientific progress dramatically and has cost the world a few nobel prize worthy ideas.